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Family-Level Sampling of Mitochondrial Genomes in
Coleoptera: Compositional Heterogeneity and
Phylogenetics
Martijn J. T. N. Timmermans, Christopher Barton, Julien Haran, Dirk
Ahrens, C. Lorna Culverwell, Alison Ollikainen, Steven Dodsworth, Peter G.
Foster, Ladislav Bocak, Alfried P. Vogler
To cite this version:
Family-Level Sampling of Mitochondrial Genomes in
Coleoptera: Compositional Heterogeneity and Phylogenetics
Martijn J. T. N. Timmermans
1,2,3,*, Christopher Barton
1, Julien Haran
1,4, Dirk Ahrens
1,5,
C. Lorna Culverwell
1,6, Alison Ollikainen
1,7, Steven Dodsworth
1,8, Peter G. Foster
1,
Ladislav Bocak
1,9, and Alfried P. Vogler
1,21Department of Life Sciences, Natural History Museum, London, United Kingdom
2Department of Life Sciences, Imperial College London - Silwood Park Campus, Ascot, United Kingdom
3Department of Natural Sciences, Middlesex University, Hendon Campus, London, United Kingdom
4Present address: INRA, UR633 Zoologie Forestie`re, Orle´ans, France
5Zoologisches Forschungsmuseum Alexander Koenig Bonn, Bonn, Germany
6Present address: Haartman Institute, Haartmaninkatu 3, University of Helsinki, Helsinki, Finland
7
Present address: Department of Medical Genetics, Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
8
Present address: Department of Comparative Plant and Fungal Biology, Royal Botanic Gardens, Kew, Richmond, Surrey, United Kingdom
9
Department of Zoology, Faculty of Science UP, Olomouc, Czech Republic *Corresponding author: E-mail: m.timmermans@mdx.ac.uk.
Accepted: November 28, 2015
Data deposition: This project has been deposited in NCBI (www.ncbi.nlm.nih.gov) under accession numbers provided inSupplementary Table S1.
Abstract
Mitochondrial genomes are readily sequenced with recent technology and thus evolutionary lineages can be densely sampled. This permits better phylogenetic estimates and assessment of potential biases resulting from heterogeneity in nucleotide composition and rate of change. We gathered 245 mitochondrial sequences for the Coleoptera representing all 4 suborders, 15 superfamilies of Polyphaga, and altogether 97 families, including 159 newly sequenced full or partial mitogenomes. Compositional heterogeneity greatly affected 3rd codon positions, and to a lesser extent the 1st and 2nd positions, even after RY coding. Heterogeneity also affected the encoded protein sequence, in particular in the nad2, nad4, nad5, and nad6 genes. Credible tree topologies were obtained with the nhPhyML (“nonhomogeneous”) algorithm implementing a model for branch-specific equilibrium frequencies. Likelihood searches using RAxML were improved by data partitioning by gene and codon position. Finally, the PhyloBayes software, which allows different substitution processes for amino acid replacement at various sites, produced a tree that best matched known higher level taxa and defined basal relationships in Coleoptera. After rooting with Neuropterida outgroups, suborder relationships were resolved as (Polyphaga (Myxophaga (Archostemata + Adephaga))). The infraorder relationships in Polyphaga were (Scirtiformia (Elateriformia ((Staphyliniformia + Scarabaeiformia) (Bostrichiformia (Cucujiformia))))). Polyphagan superfamilies were recovered as monophyla except Staphylinoidea (paraphyletic for Scarabaeiformia) and Cucujoidea, which can no longer be considered a valid taxon. The study shows that, although compositional heterogeneity is not universal, it cannot be eliminated for some mitochondrial genes, but dense taxon sampling and the use of appropriate Bayesian analyses can still produce robust phylogenetic trees.
Key words: mitogenomes, long-range PCR, rogue taxa, RY coding, mixture models, PhyloBayes.
Introduction
Mitochondrial genomes have often been perceived as unreli-able phylogenetic markers due to poor recovery of the expected relationships, in particular in early studies that were compromised by sparse taxon sampling (Bernt et al.
2013;Simon and Hadrys 2013). In insects, high rates of nu-cleotide change in mitochondrial genomes, together with high adenine-thymine (AT) content and constraints of protein function, limit the type of character variation and result in high levels of homoplasy (Talavera and Vila 2011). As rates of
GBE
ßThe Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
change and nucleotide composition vary among lineages, mitogenome sequences are exposed to long-branch attrac-tion, which confounds phylogenetic inferences. This phenom-enon has received particular attention in studies of Coleoptera (beetles) showing that compositional heterogeneity is perva-sive (Sheffield et al. 2009;Pons et al. 2010;Song et al. 2010;
Bernt et al. 2013;Cameron 2014). However, although various likelihood models of DNA evolution assume stationarity, that is, an evolutionary process that keeps the character state dis-tribution uniform across lineages, recent nonhomogeneous models accommodate changes in composition over the tree (Galtier and Gouy 1998; Foster 2004; Boussau and Gouy 2006;Foster et al. 2009).
An alternative approach for accommodating complex char-acter variation is the site-heterogeneous CAT model imple-mented in PhyloBayes (Lartillot et al. 2009), which infers an infinite number of substitution processes (classes) from the empirical data, each of which are defined by different equilibrium frequencies of nucleotides or amino acids. This “heterogeneous mixture model” is widely used for the anal-ysis of protein sequences, and was shown to reduce the sus-ceptibility to long-branch attraction (Lartillot et al. 2007;
Talavera and Vila 2011;Li et al. 2015). When applied to the Coleoptera, the use of PhyloBayes greatly improved the tree to match expected taxonomic groups over other models applied to the nucleotide sequences. For example, in the analysis of
Timmermans et al. (2010)the single representative of the sub-order Archostemata (genus Tetraphalerus) was placed incor-rectly in a derived position within the suborder Polyphaga under various coding schemes and optimality criteria, as also observed in other studies (Pons et al. 2010;Song et al. 2010), but under the CAT model it was placed correctly outside of Polyphaga. Likewise, the CAT model was more successful than other approaches in recovering the major clades including the infraorders (“series”) within the Polyphaga (Timmermans et al. 2010). To some extent the effect of these mixture models can be achieved by partitioning the data according to a priori de-termined character sets and applying an independent GTR model, which can be implemented using the RAxML likelihood method (Stamatakis 2006).
The misleading signal from compositional heterogeneity is not produced by all nucleotides in equal measure, as rates are constrained in 1st and 2nd codon positions, which prevents rapid divergence in base composition (Song et al. 2010;
Talavera and Vila 2011). Many previous studies therefore ex-cluded 3rd codon positions from the analysis to reduce the effects of compositional heterogeneity. In addition, purine-pyrimidine (RY) coding can be used, which removes the AT versus GC compositional information in the assessment of character variation (Hassanin 2006). Finally, compositional heterogeneity has sometimes been shown to be concentrated in particular portions of the mitochondrial genome or in par-ticular species or subclades, and hence data exclusion has been recommended, for example, omitting individual genes
that produce trees in conflict with the topology obtained from the full data (Talavera and Vila 2011). However, the link be-tween topological incongruence among data partitions and compositional heterogeneity has not been widely explored. In Coleoptera, substitution rates are well known to differ among mitochondrial genes (Vogler et al. 2005;Pons et al. 2010), but the level of compositional heterogeneity has not been com-pared among genes.
With the application of high-throughput sequencing tech-niques, the number of mitochondrial genomes available for these analyses is increasing rapidly. The resulting denser taxon sampling may improve the estimation of molecular rates and variation in base composition, and thus result in improvements in estimates of tree topology, in particular through reduced long-branch attraction of convergent character variation. Here we generate a large set of mitochondrial genomes for the Coleoptera to test if the known problems for phylogenetic inference in this group previously ascribed to compositional heterogeneity can be overcome by denser taxon sampling. We also examine if high compositional heterogeneity affect-ing some terminals weakens the recovery of monophyletic groups and produce erroneous relationships. Not all such groups are expected to be strongly supported, but instead the effect of compositional heterogeneity may mainly reduce levels of support for otherwise well founded groups, and as their placement is ill-defined by the data they may appear as nuisance “rogue taxa” weakening an otherwise well supported topology (Wilkinson 1996). Their removal may reduce the compositional heterogeneity across the data and improve the overall tree topology.
We thus examine the evidence for compositional hetero-geneity within and among genes, and test its impact on the topology. However, measuring compositional heterogeneity itself is challenging. A chi-square test (implemented in PAUP;
Swofford 2002) has been widely used to assess if nucleotide composition in a data matrix is homogeneous, but this test suffers from a high probability of Type II error (the null hypoth-esis of homogeneity is false but fails to be rejected) because it does not assume phylogenetic relatedness (Kumar and Gadagkar 2001). As the effects of common ancestry are inte-gral to the test quantity, they should be part of the null dis-tribution as well. Such a null hypothesis can be generated by simulating data on the tree topology and model parameters of the empirical data, and the heterogeneity in the empirical data is then assessed against this distribution from simulations, again using the chi-square as a test quantity (Foster 2004). This approach is used here to address how compositional het-erogeneity in different partitions of the mitogenome data matrix (e.g., various genes, codon positions, clades) affects the accuracy of the tree. We also examine whether these biases can be overcome by analyses of the translated protein sequences and by removal of certain data partitions or diver-gent lineages, including potential rogue taxa. We show that densely sampled mitogenomes can provide a well-supported
tree for the Coleoptera, even under moderate levels of com-positional heterogeneity, and these relationships are best captured by the mixture models in PhyloBayes. The new tree consolidates the phylogenetic conclusions from previous stud-ies and resolves several questionable nodes defining coleop-teran superfamily and family-level relationships.
Materials and Methods
Sampling and Laboratory Procedures
Mitogenome sequences were generated from long-range PCR amplicons using the Roche/454 sequencing platform. Specimens were selected for uniform coverage of major line-ages of Coleoptera from existing DNA extractions of various age and quality of preservation (Hunt and Vogler 2008;Bocak et al. 2014), in addition to newly collected specimens, resulting in highly variable PCR success that limited the taxon choice (supplementary table S1, Supplementary Material online). Amplification primarily targeted a large cob to cox1 fragment of ~10 kb. The remainder of the mitogenome was amplified using primer sites in the cox1 and cob genes, to include the rRNA genes and the control region, but amplification success was lower (supplementary table S2, Supplementary Material
online). Primers used are described inTimmermans et al. (2010). Sequence reads were assembled using the MIRA or Newbler software as described previously (Timmermans et al. 2010;
Haran et al. 2013) and the longest contig obtained with either assembler was retained. tRNA genes were annotated with COVE using beetle-specific covariance models (Timmermans and Vogler 2012). Protein-coding gene sequences were anno-tated using existing Coleoptera mitochondrial genomes as ref-erence in Geneious (http://www.geneious.com/, last accessed December 17, 2015). For the rRNA genes, sequences were ex-tracted from the newly generated and previously published mitogenome sequences, using BLAST searches on a fasta for-matted database with methods described inBocak et al. (2014). The taxonomic classification, voucher ID, GenBank accession numbers, and geographic origin for each specimen are given insupplementary table S1,Supplementary Materialonline.
Phylogenetic Inference
The 13 protein-coding genes were aligned with ClustalW using the transAlign wrapper (Bininda-Emonds 2005). The cox1 gene was split into the 50
“barcode” region (Hebert et al. 2003) and the 30
region widely used in Coleoptera systematics usually amplified with the Pat and Jerry primers (Simon et al. 1994). This was to account for the fact that the two PCR fragments with different amplification success are confined to the 50or 30
ends for the short and long fragment, respectively. The two rRNA genes were aligned using MAFFT v. 7 (Katoh et al. 2009) under default parameters on the server http://mafft.cbrc.jp/ alignment/software/, last accessed December 17, 2015. Protein-coding alignments were edited, trimmed, and
translated with Mesquite v. 2.75 (Maddison WP and Maddison DR 2014). The final concatenated matrix consisted of the 13 protein-coding genes (14 regions taking into account the split cox1 gene) and 2 rRNA genes, with a minimum of 9 protein-coding genes represented in all taxa. All tree searches and analyses of evolutionary patterns were done without further outgroups, except for one case of a PhyloBayes analysis designed to test the basal branching order in the light of non-Coleoptera outgroups. Mutational saturation was assessed in Dambe5, using a simulation-based analysis of the critical sub-stitution saturation beyond which the correct tree is unlikely to be recovered (Xia 2013).
Different partitioning strategies were compared for the nu-cleotide data matrix of protein-coding genes, by calculating likelihood scores on a fixed topology generated in RAxML (Stamatakis 2006). Twelve partitioning schemes for the protein-coding genes were compared, ranging from unparti-tioned to a maximum of 42 partitions (by gene + codon posi-tion). Likelihood scores were compared with reference to the complexity of the partitioning schemes using the Akaike Information Criterion (AIC). Bayes Factors and Relative Bayes Factors (RBF) were calculated according toCastoe et al. (2005). Phylogenetic trees were generated using ML and Bayesian methods for partitioned and unpartitioned data sets. All RAxML trees were generated at the CIPRES webserver, (Miller et al. 2010; https://www.phylo.org/, last accessed December 17, 2015) under the GTRCAT model of nucleotide substitution, which approximates a GTR + model with a re-duced computational cost (Stamatakis 2006). Where relevant, node support was assessed using a rapid bootstrap algorithm implemented in RAxML with 500 replicates.
PhyML (Galtier and Gouy 1998;Guindon and Gascuel 2003) was run on the ATGC webserver (Guindon et al. 2010;www. atgc-montpellier.fr, last accessed December 17, 2015) and used a GTR substitution model using eight rate categories. The gamma shape parameter and the proportion of invariable sites were estimated from the data. To infer relationships under the nonhomogeneous model of Galtier and Gouy (1998), nhPhyML (Boussau and Gouy 2006) was used, again using eight rate categories. Topology, gamma shape parame-ter, and transition/transversion rates were evaluated, but no final optimization of parameters such as branch lengths was performed (setting: -quick = y). As starting tree for tree searches in PhyML and nhPhyML, we used the RAxML tree of the com-plete, partitioned data set rooted on the Archostemata. Both analyses used the Nearest Neighbor Interchange algorithm.
Finally, the translated data matrix was subjected to Bayesian analysis with PhyloBayes 3 under the CAT-Poisson model (Lartillot et al. 2009). Two Markov chain Monte Carlo chains were run after the removal of constant sites from the alignment. This Bayesian analysis was repeated with outgroups included. These outgroups were from three orders of Neuropterida, the presumed sister lineage of Coleoptera, and were obtained from GenBank (Accession
numbers: NC_011277, NC_011278, NC_013257, NC_015 093, NC_021415, NC_023362, NC_024825, NC_024826). PhyloBayes tree searches were also conducted on the CIPRES webserver.
The R package “ape” (Paradis et al. 2004) was used to obtain root-to-tip branch lengths from the RY-coded ML and the Bayesian amino acid trees. Mean values and standard deviations of branch lengths were calculated for each subor-der and each of the polyphagan subfamilies.
Compositional Heterogeneity
Compositional heterogeneity in data matrices based on the protein-coding genes was assessed as described in Foster (2004), using the chi-square statistic. Significance was as-sessed using a null distribution generated by simulations on the ML tree with branch lengths and a value (a of the distribution) optimized. If the procedure is performed on the entire matrix, this presumes that there is no among-partition rate variation and that branch lengths for all partitions are the same. Since we used a homogeneous model, these values form a valid null distribution by which to assess the chi-square of the original data. RY-coded partitions were analyzed as DNA with RAxML. For simulations of protein sequences, the null distribution for assessing chi-square was generated using simulations on the corresponding ML tree and the MtArt + model (Abascal et al. 2007). Missing taxa will not contribute to the calculated chi-square value for the original data, and therefore the chi-square calculations were done without the taxa affected by missing data for a given locus. Assessment of significance was based on tail area probabilities Pt, and a value
of 0.05 or less was taken to show compositional heterogene-ity. We also used the conventional chi-square test of compo-sitional heterogeneity for comparison. The analysis of heterogeneity was conducted on the ingroup sequences only.
Identification of Rogue Taxa
The RogueNaRok algorithm (Aberer et al. 2013) was used to identify rogue taxa (Wilkinson 1996), that is, those taxa that, if excluded from the tree searches, yield a pruned consensus tree with increased support values. Using an RAxML tree on RY-coded data (see Results), two settings were tested, allow-ing either one taxon (run #1) or two taxa to be pruned simul-taneously (run #2). The change of support values was assessed on the tree obtained from the ML tree. To handle the effect of interaction between long branches we ran an analysis with a maximum dropset size of 3.
Results
Mitochondrial Genomes of Coleoptera
Full or partial mitochondrial genomes were newly generated for 159 taxa by sequencing LR-PCR fragments. In addition, 86 partial or full mitogenomes from previously published sources
were incorporated for a combined data set of 245 terminals. The small PCR fragment was represented by fewer taxa, and thus nad2, cox1-5’, and the 12S and 16S rRNA (rrnS and rrnL) genes were missing for 148, 142, 169, and 139, respectively, while the remaining set was nearly complete for all taxa ( sup-plementary table S2,Supplementary Materialonline), and 51 taxa were represented by the complete set of genes. All ter-minals had a minimum of 9 protein-coding fragments (of 14 fragments in total, including 2 parts of cox1) and the average data completion was 13.1 fragments, with a total sequence length of 6,202–11,717 bp. The aligned supermatrix con-sisted of 11,141 characters for protein-coding genes, and 12,271 characters when the 2 rRNA genes were included. The two supermatrices contained 15.27% and 20.29% miss-ing data, respectively. The samplmiss-ing covered all 4 suborders of Coleoptera, 15 superfamilies of Polyphaga (only leaving out the Derodontoidea for which no sequences were available), and a total of 97 families.
We found several gene order rearrangements in addition to those already described byTimmermans and Vogler (2012), which mainly affected the ARNSEF (Ala, Arg, Asn, Ser, Glu, Phe) cluster between the nad3 and nad5 genes. Three species of Chrysomelidae (Exema, Crytocephalus, and Pseudocolapsis) had the order of tRNAAlaand tRNAArgreversed (RANSEF). This state had previously been observed in Peploptera (Timmermans and Vogler 2012), which was placed together with the other three suggesting a single origin of this gene order but the tree topology suggests this group to be para-phyletic for Imatidium, Laccoptera, and Arescus which appar-ently reverted to the ancestral state. In addition, the RANSEF gene order was also observed in a subclade of the distantly related melyrid lineage (Cleroidea), represented by four spe-cies, while it was also previously reported from Naupactus (Curculionidae) (Song et al. 2010) and other weevil species (Haran et al. 2013;Gillett et al. 2014). A further rearrange-ment of this tRNA cluster was seen in Cyphonistes (Scarabaeidae: Dynastinae) (ANRSEF). This represents a new state not previously observed in Coleoptera. Finally, the order of the genes for tRNALysand tRNAAsp(KD) located between the cox2 and atp6 loci was reversed (DK) in Sphindus (Sphindidae). In addition to these various rearrangements, we observed two anticodon changes, including a GCG to GCU change in the tRNAAla anticodon, present in all Polyphaga, and a change from CUU to UUU of the tRNALys anticodon, present in all Chrysomeloidea and also two species of Curculionoidea (only one of them represented in the tree) (fig. 1).
Model Testing
Partitioning greatly improved the likelihood scores. The model testing under the AIC identified the most complex partitioning scheme (partitioning by gene and codon) as the most favor-able, with highly significant Bayes Factors against all other
FIG. 1.—The tree of Coleoptera based on protein-coding genes obtained with PhyloBayes. Major groups at the level of superfamily and above are
labeled, and each superfamily is illustrated with a representative line drawing. Numbers on the branches represent posterior probabilities. Changes in anticodons of tRNALys(in Chrysomeloidea and in taxon labeled with blue triangle) and tRNAAla(in Polyphaga and taxa labeled with orange triangles) and
several newly discovered gene order changes are mapped on the tree.
partitioning schemes (table 1). However, various partitioning schemes contributed in different ways. Based on the AIC, partitioning by forward and reverse strand resulted in a major improvement over the unpartitioned model, and this could be improved only slightly by further partitioning by genes. Separating the genes according to those genes most strongly affected by compositional heterogeneity (see below) had little impact on the AIC score. In contrast, partitioning by codon positions had a strong effect, and this was further improved by partitioning according to coding on the forward and reverse strands, that is, using six partitions. The likelihood score for this partitioning scheme was closest to that from the full partition-ing by gene and codon, and accordpartition-ing to the RBF, it is the most efficient way of improving the likelihood scores per
parameter added to the model. However, based on the Bayes Factor the model distinguishing 42 partitions was still significantly better.
Tests of Compositional Heterogeneity
The conventional chi-square test showed that the data are heterogeneous (P = 0 that the data are homogeneous). We then asked if heterogeneity is uniform across the data parti-tions by performing the test separately on each gene partition and codon position. The 3rd codon positions were heteroge-neous for all genes (table 2) and also showed significant levels of saturation for about half of the gene partitions ( supplemen-tary table S3,Supplementary Materialonline). Therefore they were not considered further for tests of heterogeneity. In
Table 2
Compositional Heterogeneity in Mitogenomes
Conventional Chi-square Foster (2004)
No rogue Protein
n missing 1st 2nd 1st RY 1st RY 2nd 1st two-state 1st RY 2nd 1st two-state
atp6 22 0.0999 1 1 1 1 0.2 1 1 0.21 1 atp8 1 1 1 1 1 0.36 0.53 1 0.24 0.51 0.02 cox1–50 142 1 1 1 1 1 1 1 1 1 0.85 cox1–30 43 1 1 1 1 1 0.95 1 1 0.99 1 cox2 1 1 1 1 1 1 1 1 1 1 1 cox3 2 1 1 1 1 1 0.82 1 1 0.85 0.98 Cytb 1 0.006 1 1 1 0.99 0.94 1 0.93 0.99 0.03 nad1 8 1 1 1 1 1 0.34 1 0.98 0.42 0.79 nad2 148 0 0.981 1 1 0 0 0.5 0 0 0 nad3 2 1 1 1 1 0.22 0.12 1 0.14 0.22 0.45 nad4 5 0 1 1 1 0.01 0.01 1 0 0 0 nad4L 5 1 1 1 1 0.96 0.95 1 0.96 0.99 0.73 nad5 5 0 1 1 1 0 0 1 0 0 0 nad6 5 0 1 1 1 0 0 1 0.01 0 0
NOTE.—Each gene was tested for the probability that the data are homogeneous and P values are provided in the table, separately for 1st and 2nd codon positions. Significance of the chi-square statistic was assessed either with the chi-square curve (“conventional chi-square”) or using a null distribution as described inFoster (2004). Note that four loci generally have low probability of homogeneity throughout. n missing, mitogenomes in the matrix not sequenced for a locus; no rogue, analysis conducted with rogue taxa omitted; protein, analysis based on amino acid sequence.
Table 1
Likelihood and AIC Values under Various Partitioning Schemes
Partitioning No. of Partitions Parameters (k) ln(L) AIC "AIC 2 ln "BF RBF
None 1 9 1,279,328.877 2,558,675.754 105,496.41 21.76 0.059 Forward/Reverse 2 18 1,258,902.112 2,517,840.225 64,660.88 20.79 0.058 Homogeneous/Heterogeneous 2 18 1,273,139.835 2,546,315.669 93,136.33 21.51 0.060 Gene 14 126 1,256,482.92 2,513,217.84 60,038.51 20.64 0.082 Codon 1 + 2 + 3 3 27 1,251,864.871 2,503,783.742 50,604.41 20.30 0.058 Codon 1 + 2 + 3 + Forward/Reverse 6 54 1,229,360.303 2,458,828.606 5,649.26 16.11 0.050
Gene codon 42 378 1,226,211.669 2,453,179.339 n/a n/a n/a
NOTE.—The likelihood of the data under each partitioning scheme was assessed on the fixed topology of a randomized parsimony tree under a GTR + G model, with the number of partitions, free parameters, and ln(L) scores used in the calculations given. AIC refers to the decrease in likelihood relative to the most complex model (partitioning by gene and codon). Values for 2 ln BF10>10 are usually considered to be highly significant. RBF was calculated according toCastoe et al. (2005)as 2 ln BF10/ parameters, to penalize greater model complexity.
contrast, all 2nd codon position partitions appeared homoge-neous by this test. The 1st codon positions failed for some genes, notably cytb, nad2, nad4, nad5, and nad6, but showed compositional homogeneity in the others. When the 1st codon positions were RY recoded, the data set as a whole was still heterogeneous (P = 0), but heterogeneity was no longer apparent in the 1st codon positions when tested for each gene individually (table 2).
The data were also assessed against data simulated under a homogeneous model (Foster 2004), which revealed heteroge-neity (P < 0.05) in 2nd codon positions in genes nad2, nad4, nad5, and nad6, despite appearing homogenous in the con-ventional chi-square test. The RY-recoded 1st positions re-mained compositionally homogeneous. However, it could be argued that using a two-state model would be more valid for analysis of RY-coded matrices, rather than calculations with DNA models. We found that this approach detected highly significant levels of heterogeneity in the nad2, nad4, nad5, and nad6 genes that were already implicated in 2nd position heterogeneity above (table 2). Finally, we conducted the test of heterogeneity on the translated protein sequence. This showed that out of 14 gene partitions, six were heteroge-neous (P < 0.05), and eight were not. The highest level of significance was again observed for nad2, nad4, nad5, and nad6 (table 2).
The RogueNaRok algorithm identified 14 (run #1) and 30 (run #2) taxa as being inconsistently placed when investigating the placement of a single terminal or a set of two terminals, respectively, for a total of 33 rogue taxa (supplementary table S4,Supplementary Materialonline). Compositional heteroge-neity was investigated for a reduced data set that had these 33 taxa excluded. The results were very similar to those ob-tained with the full matrix, with heterogeneity in 2nd positions and in the two-state model of RY-recoded 1st position sites limited to nad2, nad4, nad5, and nad6 partitions (table 2). Rogue taxa instead seemed to be affected by slightly lower data completion, specifically the sequences for the short amplicon coding for nad2 and cox1-5’, which was missing from 22 or 23 respectively of the 33 rogue taxa. Yet, the average completion of the data set for rogue taxa was similar to the complete data set (12.24 vs. 12.40 protein-coding loci per taxon;supplementary table S4, Supplementary Material
online), and >120 other taxa in the matrix were also lacking the short fragment (supplementary table S2,Supplementary Materialonline).
Phylogenetic Analysis
A series of phylogenetic analyses was conducted to assess the effects of nonhomogeneity on tree topology. We used three different approaches for tree searches to make use of the available phylogenetic methods, and scored these trees for about 30 nodes defining deep relationships that were ex-pected based on previous work or appeared noteworthy
because they differed among the tree searches here (table 3
andsupplementary table S4,Supplementary Materialonline). We used PhyML and nhPhyML for assessing the sensitivity of the topology to the introduction of branch-specific parame-ters in the nonhomogeneous model. The tree generated with PhyML was unsatisfactory in many regards due to the failure of recovering several key groups, including the large suborders Adephaga and Polyphaga, four of the five infraorders, and the superfamilies in the species-rich Cucujiformia. We then com-pared the topology from the nhPhyML model, which adds a separate parameter for the nucleotide composition for each branch. The nhPhyML tree (supplementary fig. S1,
Supplementary Materialonline) was greatly improved, includ-ing the monophyly of the suborders and all infraorders. However, in the Cucujiformia only the (reciprocal) monophyly of Tenebrionoidea and Lymexyloidea was recovered, whereas paraphyly remained surrounding Chrysomeloidea, Curculionoidea, and Cucujoidea.
The RAxML software was used to assess nonhomogeneity across the data (not across the tree, as in nhPhyML) imple-menting independent GTR models for different partitions of the matrix (although without allowing among-partition rate variation that is not implemented in this software). A tree from the unpartitioned data had many of the same undesirable features as the PhyML tree, including the nonmonophyly of Adephaga and Polyphaga, although with a better outcome overall including the recovery of three of five infraorders. Partitioning the data according to the 42 codon and gene partitions improved the topology by recovering all 4 subor-ders, the 5 infraorsubor-ders, and most superfamilies, but problems with the recovery of the cucujiform superfamilies were not fully solved. The impact of including and excluding the two rRNA genes was limited (table 3andsupplementary table S4,
Supplementary Materialonline). We further used the RAxML algorithm to explore the effects of removing the most com-positionally heterogeneous data, first by removal of 3rd codon positions and RY coding of 1st positions, and in an additional search we also removed the four loci showing the greatest level of heterogeneity. Finally, we used the amino acid trans-lation (on all protein-coding genes) (table 3and supplemen-tary table S4,Supplementary Materialonline). Although most of the correctly recovered higher groupings were robust to the specific data treatment, there was a general decrease in power with the removal of data, and none of these analyses performed better than the partitioned analysis of all nucleo-tides. Notably, the removal of the rate-heterogeneous genes (nad2, nad4, nad5, nad6) resulted in the loss of monophyly of both small suborders, Myxophaga and Archostemata ( supple-mentary fig. S2, Supplementary Material online, for a tree from a matrix RY recoded for 1st positions and 3rd positions removed). Equally, the amino acid coding resulted in the fail-ure to recover several key groups, including the suborder Polyphaga that was paraphyletic due to the misplaced Tetraphalerus and Priacma (Archostemata). Hence, the
Table 3 Recov ery of Key N odes and Other Feat u res in Tr ees Obtained fr om Dif ferent A na lyses w it h PhyML , R Ax ML or Phyl oBayes (PB), Befor e and A ft er (exc l. het er ogeneous) R emov ing the Compositio n Hetero gen eou s M ar kers nad 2, nad 4, nad 5, and nad 6 Ta xon P hy ML RAx M L P hyloB ayes Phy M L nhPhyML Unpart itioned P artit ioned Par titione d no 12 S/16S RY code RY code no 1 2S/16S ( suppleme n tary fig. S 1 , Su pplementa ry Ma terial onl ine) E x cluding het e rogenous Amino acid Pl u s out groups ( fig. 1 ) No outgro ups ( supple m entary fig . S 2 , Supplement a ry Materi al online) Excludin g rogue E x cluding het e rogenous Pos ition in fig u re 3 12 3 4 4 x X 5 6 7 x 8 9 All suborde rs monoph yly e tic N Y N Y Y Y Y N N Y Y Y N Su border s rela tions hips n/a P (Ar (M + A d)) (P + A r) (M + A d) P( A r (M + A d)) P( A r (M + A d)) P( A r (M + A d)) P( A r (M + A d)) n/a (P + Ar) (M + A d ) P( M (Ar + Ad)) P( M (Ar + Ad)) P( M (A r + Ad )) P( M + Ar + A d) Gea d epha ga M* M* M* M* M* M M M* M M * M * M M* Ela ter iform ia P M P M M M M M P M M M M St aphy linifo rmia + Sc arab aeifo rmia P M M M M M M M M M M M M Sc araba e ifor mia P M M M M M M M M M M M M Bos trich iform ia P M M M M M M P M M M M M Bos trich iform ia sist e r n /a E lat Elat E lat Ela t Cu c Cuc Cuc Cuc C uc Cuc Cuc Cuc Cucu jifor mia M M M M M M M M M M M M M Cle roidea M M M M M M M P M M M M M Cer yloni d Seri e s M M M M M M M M M M M M M Nit idulid Se rie s M P M M M M M P P U P M P Cucu joid Ser ies M M M M M M M M M M P M P Nit idulid + C uc ujoid M M M M M M M M M U U M M Te nebrio noidea + Lymex yloi dea M M M M M M M M M M M M M Te n. + Lym. recipr .monophy ly N Y Y N N N N N Y Y Y Y Y Chr ysome loide a P P P P P P P P P M M M P Cur culionoi d ea P P P P P P P P N M M P P Chr ys. + Cur c. recip r. m onophy ly N N N N N N N N N Y Y Y Y N OT E .— RA xM L tr e e s we re o b ta in ed w it h th e R Y-co de d 1 st p o si ti o n s a nd 3r d p o si ti o n s re mo ve d, or o n al l d at a (in cl ud in g the rR N A ge ne s) . A ll P h yl o B a ye s tr ee s w e re co n d u ct e do n th e a m in oa ci dc o d e d m a tr ix .M , m o n o p h yl e ti c; P , pa ra ph yl et ic o r po ly ph yl et ic ; U , u n re so lv e d, co ns is te nt w it h mo no ph yl y; Y, yes , a fe a tu re is p re se n t; N, n o , a fe a tu re is n o t pr es en t. In so me ca se s, th e gr ou ps w e re re co ve red b u t wi th ce rt a in m emb e r tax a a b se n t ( )o ro th e r ta xa in cl ud ed (+) , a s in d ica te d . No te th at Sp hi nd us (S ph in di da e) w a s d is re g a rd e d w h e n sc o ri n g N it id u lid an d C uc uj id se rie s. T h e a st e ris k s m ar k the tr ee s tha t a re mo no ph yl et ic fo r G ea de ph ag a o nl y if Ha br od er a (C ic in de lida e) is d is re g a rd e d .
RAxML analysis was not greatly distorted by compositional heterogeneity and instead suffered more from the loss of data when the most heterogeneous positions were removed. Finally, the CAT model in PhyloBayes also partitions the data, but unlike the RAxML analysis these partitions are not determined a priori but are estimated from the data them-selves. The resulting tree (fig. 1) showed most of the features of the trees from the partitioned RAxML analysis, but also recovered the two large superfamilies Curculionoidea and Chrysomeloidea that were otherwise polyphyletic with respect to each other and included portions of Cucujoidea in all other analyses (supplementary table S4, Supplementary Material
online). This tree also recovered a different relationship of the four suborders, linking Adephaga with Archostemata and not Myxophaga, and when rooted with the neuropteroid outgroups, the relationships were (Neuropteroid (Polyphaga (Myxophaga (Archostemata + Adephaga)))), consistent with the findings of transcriptome analyses (Misof et al. 2014). Removal of the four heterogeneous nad genes did not greatly change the tree topology, although the resolution was re-duced, indicating the loss of phylogenetic signal ( supplemen-tary table S4, Supplementary Material online). Finally, the Bayesian analysis was run again after removal of rogue taxa, which produced a tree very similar to that based on the com-plete data set, with the main improvement simply due to the absence of the inconsistently placed rogue taxa themselves. For example, only after removing several rogue taxa, in par-ticular the divergent sequence for Sphindus (Sphindidae), the Nitidulid and Cucujid series of Cucujoidea each resolved as monophyletic and combined they were the sister group to Curculionoidea + Chrysomeloidea (supplementary table S4,
Supplementary Materialonline).
The Branch Length across Superfamilies
Root-to-tip branch lengths were investigated on the RY-coded ML tree (supplementary fig. S2, Supplementary Material
online) and the Bayesian amino acid tree (supplementary fig. S3, Supplementary Material online) for each suborder and polyphagan superfamily (fig. 2 and supplementary fig. S4,
Supplementary Material online). Variation among these groups was very similar for each data set. The Adephaga and Myxophaga showed substantially shorter branches than the two other suborders Archostemata and Polyphaga. Shorter branches were found in several polyphagan superfa-milies, compared with Bostrichiformia and all superfamilies of Cucujiformia, which are sister groups in most analyses and occupy a derived position in the tree. Within some superfami-lies branch lengths were highly variable, for example, the two sequences of Passalidae with extremely long branches, which were responsible for shifting the average branch length in Scarabaeoidea beyond the rate of other staphyliniform lineages. Similarly high variation in branch lengths was found in Elateroidea due to extremely long branches in
Trixagus and Mastinocerus. Extremely long branches compared with their sister taxa were found additionally in Melittomma (Lymexylidae), Sphindus (Sphindidae), Cassidinae (Chrysomelidae), and others. In addition, the rogue taxa had a tendency to exhibit faster rates of nucleotide change, with an average branch length higher than for the complete set of taxa (0.86997 vs. 0.73820) and many termi-nals in the top part of the range of root-to-tip distances, and a generally higher proportion of rogue taxa was found in super-families with higher branch length variability (supplementary table S5,Supplementary Materialonline).
Discussion
This study generated a large number of new mitogenome sequences for the Coleoptera that more than doubles the available sequences and now permits an analysis of molecular evolution at the resolution of the family level. Early studies of Coleoptera using mitochondrial genomes noted the great het-erogeneity in nucleotide composition and molecular rate that apparently misled the trees (Pons et al. 2010; Song et al. 2010). The sparse taxon sampling of studies conducted with conventional Sanger sequencing may have exacerbated these problems. If nucleotide heterogeneity is high and localized in the tree, and if similar composition arises convergently, there will be a tendency to create biases that overwhelm the phy-logenetic signal. Already denser taxon sampling, the removal of synonymous codon positions, and the use of protein se-quences were shown to partly overcome these problems (Timmermans et al. 2010). This is confirmed here for a much greater set of mitogenomes. However, it was not clear if the improved phylogenetic inference is correlated with reduced compositional heterogeneity, and to what degree heterogeneity can be reduced by removal of the most affected bases and by translation to protein sequences that might reduce the compositional bias from different codon usage.
Previous studies have established the distribution of com-positional heterogeneity using the disparity index ID (Song
et al. 2010) that is based on the differences in substitution pattern for pairs of sequences deviating from expectations under a process of uniform nucleotide change. This analysis produced a measure of compositional heterogeneity for each terminal relative to other taxa in the data set and found that the more densely sampled Polyphaga exhibit the lowest cu-mulative disparity across all pairwise comparisons, whereas Tetraphalerus as the single representative of Archostemata had the highest disparity when summing the IDvalues from
comparisons with all other taxa (Song et al. 2010). These find-ings suggest that compositional heterogeneity is increased be-tween distantly related taxa and therefore greater sampling density, as available in the Polyphaga, ameliorates the prob-lem, although residual heterogeneity remains even in very
densely sampled mitogenome trees, for example, in a tree of ~100 taxa in the family Curculionoidea (Gillett et al. 2014).
In this study, rather than using pairwise comparisons, het-erogeneity was assessed for the matrix as a whole, but only after the data were partitioned by gene. This analysis showed that compositional heterogeneity is concentrated in four genes, all of them NADH dehydrogenases. Two of these (nad4 and nad5) are on the reverse strand, while nad6, but not nad2, is in proximity to these genes, encoded by the for-ward strand. It is intriguing that these genes did not deviate in their impact on model fit in the partitioning, as splitting them and all others did not greatly improve the likelihood of the model (table 1). This was in contrast to data partitioning by forward and reverse strand that accounted for a large im-provement in statistical fit in GTR models (i.e., under compo-sitional homogeneity assumed by the GTR, and hence indicating different evolutionary patterns on either strand unrelated to compositional heterogeneity). Although RY recoding reduced the problem of compositional heterogene-ity, it remains strong if applying a two-state model. Equally, the problem of compositional heterogeneity was not removed by using the amino acid sequences. Nucleotide bias has been shown to feed through the amino acid level; for example,
there is a correlation of AT or GC-rich mitogenomes with a prevalence of particular amino acids, which was established mainly in inter-phyla and inter-order comparisons of mitogen-omes greatly differing in base composition (Foster et al. 1997;
Bernt et al. 2013;Li et al. 2015), and this seems to be con-firmed here at a lower hierarchical level. The finding that pre-dominantly the nad genes were affected by heterogeneity, which are functionally linked, might suggest that variation in the protein level and possible covariation in the NAD protein complex drives compositional heterogeneity, rather than some unspecified genomic process driven by strand bias. Evolutionary shifts in mitochondrial genes have been associ-ated with positive selection, for example, with changes to re-spiratory function (Tomasco and Lessa 2011), although because compositional heterogeneity in the four affected genes is encountered in all codon positions, other explanations due to gene-wide effects may also apply.
We also tested if exclusion of the so-called rogue taxa im-proves the tree topology for the remaining taxa. There are different reasons for a taxon to be rogue, and here we spec-ulated that compositional heterogeneity is a contributing factor, but their removal had virtually no impact on the degree of compositional heterogeneity in the data. It is not clear what causes their inconsistent placement instead, but multiple factors probably contribute. Rogue taxa have a slightly lower representation of the nad2 and cox1-5’ markers located on the shorter PCR fragment than the matrix as a whole. Rogue taxa also have a tendency to show higher rates of nucleotide variation, which appears to interfere with stability of their placement on the tree. These factors may affect the strength of the signal through limited data or weak long-branch attraction.
Taken together, the compositional heterogeneity in Coleoptera mitogenomes is moderate and it is spread over the tree somewhat evenly, and therefore heterogeneity per se might not have a great impact on the difficulties to recover the correct tree, in particular for those lineages where the true phylogenetic signal is strong. We can see the effect of nucle-otide composition alone if we construct a tree based on the composition of each taxon. Therefore we constructed dis-tance matrices based on nucleotide compositions and made neighbor joining trees based on the distance matrices with the BIONJ algorithm (Gascuel 1997). Using 100 bootstrap repli-cates, a consensus tree showed hardly any strong (>50%) support for any lineage, and most support was weak at <20% (data not shown). This confirms the idea that the effect of compositional biases on the tree topology is moder-ate and not localized.
Heterogeneity and Tree Topology
The three major approaches using the PhyML, RAxML, and PhyloBayes algorithms are implementations of very different likelihood models and search strategies, whose performance
FIG. 2.—Mean branch length for major groups at suborder and
su-perfamily levels. The corresponding numbers for the amino acid tree are provided insupplementary figure 4,Supplementary Materialonline.
was assessed in the light of information about the level of compositional heterogeneity. As the tree of Coleoptera re-mains insufficiently known, the quality of different models cannot be tested against a “true tree,” but the knowledge on coleopteran phylogeny is now sufficiently good to rely on the recovery of numerous well-established monophyletic groups to assess the quality and thus provides guidance on how to select the most defensible topology. In turn, the as-sessment against those “known” nodes also provides infor-mation on the less well-known parts of the tree to establish basal relationships.
Only the nhPhyML analysis provides a means for testing the effect of nonhomogeneity explicitly, as it accommodates changing the GC/AT ratio at every node in the tree (Galtier and Gouy 1998), although perhaps at the risk of overparameterization. The algorithm is implemented only for DNA data. Other tree-heterogeneous models are also imple-mented for protein sequences, such as the nonhomogeneous nhPhyloBayes, and the NDCH and the NDRH models (node-discrete composition heterogeneity and node-(node-discrete rate heterogeneity, respectively) which allow different composi-tions and different rate matrices on different branches, imple-mented in P4 (Foster et al. 2009). However, neither of these can be applied on the scale required here. The improvement obtained from nhPhyML over the homogeneous PhyML model was considerable, indicating the importance of taking into account the nonhomogeneity of nucleotide composition across the tree. This approach clearly increases the number of higher taxa recovered, although the tree remains unsatisfac-tory in some parts. We also conducted a RAxML analysis that only implements the standard GTR model (i.e., it does not parameterize tree heterogeneity), but permits partitioning of the data according to genes and codon positions. Data parti-tioning clearly improved the tree topology, to a similar degree as the use of the nonhomogeneous model in nhPhyML. However, there was no improvement after RY coding and removal of 3rd positions, while the removal of the heteroge-neous nad genes or the recoding as amino acids caused a deterioration. The only obvious improvement from omitting the 3rd position was the avoidance of long-branch attraction for two lineages in Elateriformia, Trixagus and Mastinocerus, which are members of distantly related families, yet display very long terminal branches that group them together in the RAxML tree based on all data including 3rd positions, but not in the other analyses. Interestingly, at least with the search strategy applied here, the nhPhyML analysis does not overcome this problem, suggesting that the cause of the long-branch attraction is not primarily due to nucleotide het-erogeneity of branches. The great rate acceleration in a few isolated taxa is a curious feature of mitogenome evolution of Coleoptera and affects nucleotide and amino acid variation alike (fig. 1andsupplementary figs. S2andS3,Supplementary Materialonline). There is concern that taxa affected by this increased rate are misplaced in the tree, in particular if multiple
such sequences attract each other, but for the most striking cases the removal of 3rd position suffices to avoid this type of long-branch attraction.
Finally, the CAT model generated the most defensible trees, and although the method does not address tree hetero-geneity explicitly, apparently it is best equipped to deal with the complex sequence variation in mitogenomes, as it pro-vides greater flexibility for modeling different classes of sites with independent substitution processes (Lartillot and Philippe 2004). Due to the size of the data set we used the simpler CAT-Poisson model whose estimate of global exchange rates (obtained empirically from the data) is shared by all sites. Yet, the CAT and CAT-GTR models are efficient in dealing with long-branch attraction due to their ability to account more accurately for saturation and thus the greater power for esti-mating the evolutionary process (Lartillot et al. 2007). These analyses were conducted only at the level of protein se-quences, but the improvement over other analyses is not due to the use of protein data per se. These data are also affected by compositional heterogeneity, and amino acid coding performed with RAxML did not result in any improve-ment over the analysis of the nucleotide variation (table 3and
fig. 3). These findings further support the power of the CAT model, at least at the level of divergence within the Coleoptera, where saturation may still be limited. An addi-tional conclusion from this analysis was that the removal of rogue taxa does not greatly improve the tree topologies, while the level of heterogeneity also is not reduced. Rogue taxa were, however, affected by longer average branches and hence were more prone to long-branch attraction, and their removal facilitated the recognition of higher taxa whose limits were blurred otherwise. For example, the sequence for Sphindus consistently interfered with the recognition of other lineages in Cucujoidea, and the extremely long-branched Trixagus interfered with relationships in Elateroidea. Both were recognized as rogue taxa.
Implications for the Phylogenetic Tree of Coleoptera
The tree topology obtained from mitochondrial genomes adds to the growing confidence in the principal lineages of Coleoptera attained in the last two decades (Lawrence and Newton 1995;Hunt et al. 2007;McKenna and Farrell 2009;
Bouchard et al. 2011;Lawrence et al. 2011;McKenna, Farrell, et al. 2015). A schematic summary of basal relationships from various analyses is given infigure 3. The results confirm the monophyly of the four beetle suborders; the monophyly of the infraorders within Polyphaga; the monophyly of most of Crowson’s superfamilies (Crowson 1970); and the monophyly of most families (where multiple representatives were used). The study also paints an increasingly clearer picture of the relationships of these groups to each other, in particular in the species-rich Polyphaga.
Specifically, the PhyloBayes analysis is the first to favor the sister relationship of Polyphaga to the three other suborders based on mitochondrial genes, which is supported by the transcriptome study ofMisof et al. (2014). Rooting was critical for this inference; data from ESTs (Hughes et al. 2006) and a smaller set of mitogenomes (Timmermans et al. 2010) in-cluded coleopteran ingroup taxa only and were rooted on Archostemata, which was supported by morphological studies (Beutel and Haas 2000;Friedrich et al. 2009) and by the abun-dance of fossils of this presumed earliest radiation of Coleoptera (Crowson 1960). However, rerooting these trees with Polyphaga produces the same ingroup topology as found here after inclusion of Neuropterida outgroups. All other mo-lecular studies based on mitogenomic analyses to date favored Myxophaga + Adephaga (Pons et al. 2010;Song et al. 2010;
Timmermans et al. 2010), which was also supported by the RAxML and PhyML analyses conducted here, and which could easily be explained by the convergent low evolutionary rates in both suborders (fig. 2 and supplementary fig. S4,
Supplementary Materialonline). Previous studies combining mitochondrial data with nuclear rRNA genes generally support a yet different topology of Polyphaga + Adephaga (Caterino et al. 2002;Hunt et al. 2007;Bocak et al. 2014). If indeed Polyphaga is the sister to the other suborders, this would reduce the imbalance of species diversity at the basal node of the tree, given that in previous work Archostemata and Myxophaga with less than 100 species each were thought to be the sister of all other Coleoptera and the Polyphaga, respectively.
Within Polyphaga, we confirm the Scirtidae/Clambidae grade as the earliest branching lineages in Polyphaga, as proposed by Hunt et al. (2007)and Lawrence (2001), to form the new series Scirtiformia. The Elateriformia is the sister to all remaining Polyphaga, again in agreement with studies from ESTs (Hughes et al. 2006), although the RAxML (all nucleotides) and nhPhyML analyses group them as sister to Bostrichiformia. Internal relationships of Elateriformia re-cover the three large groups Buprestoidea, Elateroidea, and Dryopoidea (=Byrrhoidea minus Byrrhidae). The latter is defined by a unique rearrangement of tRNA gene order (Timmermans and Vogler 2012), which is confirmed here for all members of this clade, but the position of Byrrhidae (Byrrhoidea) and Dascilloidea remains ambiguous ( supple-mentary table S4, Supplementary Material online). The Staphyliniformia occupying the next node is composed of three major groups (Histeroidea, Hydrophiloidea, Staphylinoidea) and also includes the Scarabaeiformia (Scarabaeoidea), which should no longer be considered at the rank of an infraorder. The staphylinoid families Leiodidae + Agyrtidae were repeatedly recovered as sister to Histeroidea, which interfered with the expected sister re-lationship of Histeroidea and Hydrophiloidea (McKenna, Wild, et al. 2015) recovered only in the PhyML analyses or when excluding the heterogeneous loci in PhyloBayes.
Bostrichiformia were split into two clades composed of Anobiidae (Anobiinae) + Ptiniidae and Dermestidae, and were the sister of Cucujiformia (except in some RAxML and nhPhyML).
Cucujiformia, the infraorder encompassing about half of all species of beetles, was always monophyletic and consists of sequential nodes defining major lineages including Cleroidea, Cerylonid series (Cucujoidea), Lymexyloidea + Tenebrionoidea, remaining Cucujoidea, Chrysomeloidea, and Curculionoidea. The Tenebrionoidea were found as sister to Lymexyloidea (Timmermans et al. 2010; Bocak et al. 2014; Gunter et al. 2014). The Cucujoidea can no longer be considered a valid taxonomic group (Hunt et al. 2007;Marvaldi et al. 2009). The mitogenomes now confirm that the Cerylonid series (Robertson et al. 2008) is only distantly related to the other cucujoid lineages, which include sets of families referred to as Nitidulid, Erotylid, and Cucujid series by Hunt et al. (2007). These groups cluster closely in the tree, either as an unresolved grade at the base of, or as sister to, the Curculionoidea + Chrysomeloidea. Only the PhyloBayes analysis recovers the reciprocal monophyly of Curculionoidea + Chrysomeloidea, which was partly interdigi-tated in all other analyses, but the monophyly of Chrysomeloidea is supported by the unique GCU tRNALys an-ticodon (fig. 1).
Conclusion
The possibilities for rapid sequencing of mitochondrial ge-nomes have brought a new perspective to the phyloge-netics of Coleoptera. Although compositional heterogeneity is pervasive in these data sets, the study joins others (Talavera and Vila 2011; Li et al. 2015) in suggesting the power of the CAT model that produced highly satisfactory trees. Partitioned likelihood models with the RAxML software were not much worse, but missed a few critical relationships apparently affected by different rates of molecular change. The problem of com-positional heterogeneity has been considered to be a major driver of long-branch attraction, and is frequently thought to be reduced by RY coding and removal of 3rd codon positions, or by using the translated protein se-quence. Here we show that these strategies cannot remove compositional heterogeneity completely, and that heterogeneity is not uniformly distributed among the various mitochondrial genes. Although removing and recoding of codon or gene partitions may reduce het-erogeneity, tree resolution and support are diminished. As it has become possible to sequence mitochondrial ge-nomes very rapidly (Gillett et al. 2014;Tang et al. 2015), the challenge is to have implementations of the Bayesian mixture models that can be used at the much larger scale required for future studies.
FIG. 3.—Schematic representation of the basal relationships from mitogenome sequences. The tree is based on the PhyloBayes analysis offigure 1, with
outgroups removed. Key nodes were scored for nine trees obtained in various analyses described intable 3.
Supplementary Material
Supplementary tables S1–S5andfigures S1–S4are available at Genome Biology and Evolution online (http:// www.gbe. oxfordjournals.org/).
Acknowledgments
This work was supported by a grant from the Leverhulme Trust to A.P.V., C.B., D.A., and L.B. (grant F/00969/H). M.T.J.N. was supported by an NERC Postdoctoral Fellowship (NE/I021578/1). We thank Junying Lim and Benjamin Linard for assistance with the editing and annotation of the mito-chondrial sequences. We are obliged to M. Balke, M. Barclay, M. Bednarik, S. Fabrizi, J. Gomez-Zurita, P. Hammond, R. A. B. Leschen, and C. Murria for donating specimens, and to two anonymous reviewers for valuable comments.
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Associate editor: Dennis Lavrov
